Triple
T3232384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zinaida Volkova |
E67769
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Zinaida |
E67769
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Zinaida | Statement: [Zinaida Volkova, givenName, Zinaida]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zinaida Context triple: [Zinaida Volkova, givenName, Zinaida]
-
A.
Lyudmila
Lyudmila is a Russian linguist and the former First Lady of Russia, known for being the ex-wife of President Vladimir Putin.
-
B.
Zinaida Volkova
chosen
Zinaida Volkova was the eldest daughter of Russian revolutionary Leon Trotsky, known for her involvement in the early Soviet intellectual milieu and her tragic death in exile.
-
C.
Nadezhda Vasilyeva
Nadezhda Vasilyeva is known primarily as a daughter of Vasily Stalin, the son of Soviet leader Joseph Stalin.
-
D.
Tatyana
Tatyana is a feminine given name of Slavic origin, particularly common in Russian-speaking countries.
-
E.
Nadezhda Orenburg
Nadezhda Orenburg is a professional women's basketball club based in Orenburg, Russia, that competes in top domestic and European competitions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaedb718c8190aae12f763033713a |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28e9f56b881908742f2aff68b2a34 |
completed | March 12, 2026, 9:59 a.m. |
Created at: March 8, 2026, 3:08 p.m.